\[E(\theta) = \sum_{i=1}^{N} \textbf{w} \cdot x_j\]

var_do_r <- TRUE

var_do_py = True

py$var_do_py

 TRUE

#Comentario

r.var_do_r

True

plot(iris$Sepal.Length, pch = 19)

plot(cars$dist, pch = 19)

plot(cars$speed, pch = 19)

g <- ggplot(cars,aes(dist,speed))+
  geom_line()
g

library(plotly)

Warning: package 'plotly' was built under R version 4.1.3


Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

ggplotly(g)

import numpy as np
def hist_and_lines():
    np.random.seed(0)
    fig, ax = plt.subplots(1, 2, figsize=(11, 4))
    ax[0].hist(np.random.randn(1000))
    for i in range(3):
        ax[1].plot(np.random.rand(10))
    ax[1].legend(['a', 'b', 'c'], loc='lower left')
    # fig.patch.set_alpha(0.0)
    # ax[0].patch.set_alpha(0.0)
    # ax[1].patch.set_alpha(0.0)
    
hist_and_lines()
    
plt.show()

sns.barplot(x=["A", "B", "C"], y=[1, 3, 2])

plot(iris$Sepal.Width ~ iris$Species, col = c('red','blue','green'))

ML Project

1 Apresentação do Problema

2 Modelo 1

3 Modelo 2

4 Modelo 3

5 Comparando Modelos

6 Conclusão